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This is an another highlighter implementation.

<h2>Features</h2>
<ul>
<li>fast for large docs</li>
<li>support N-gram fields</li>
<li>support phrase-unit highlighting with slops</li>
<li>support multi-term (includes wildcard, range, regexp, etc) queries</li>
<li>need Java 1.5</li>
<li>highlight fields need to be stored with Positions and Offsets</li>
<li>take into account query boost and/or IDF-weight to score fragments</li>
<li>support colored highlight tags</li>
<li>pluggable FragListBuilder / FieldFragList</li>
<li>pluggable FragmentsBuilder</li>
</ul>

<h2>Algorithm</h2>
<p>To explain the algorithm, let's use the following sample text
 (to be highlighted) and user query:</p>

<table border=1>
<tr>
<td><b>Sample Text</b></td>
<td>Lucene is a search engine library.</td>
</tr>
<tr>
<td><b>User Query</b></td>
<td>Lucene^2 OR "search library"~1</td>
</tr>
</table>

<p>The user query is a BooleanQuery that consists of TermQuery("Lucene") 
with boost of 2 and PhraseQuery("search library") with slop of 1.</p>
<p>For your convenience, here is the offsets and positions info of the 
sample text.</p>

<pre>
+--------+-----------------------------------+
|        |          1111111111222222222233333|
|  offset|01234567890123456789012345678901234|
+--------+-----------------------------------+
|document|Lucene is a search engine library. |
+--------*-----------------------------------+
|position|0      1  2 3      4      5        |
+--------*-----------------------------------+
</pre>

<h3>Step 1.</h3>
<p>In Step 1, Fast Vector Highlighter generates {@link org.apache.lucene.search.vectorhighlight.FieldQuery.QueryPhraseMap} from the user query.
<code>QueryPhraseMap</code> consists of the following members:</p>
<pre class="prettyprint">
public class QueryPhraseMap {
  boolean terminal;
  int slop;   // valid if terminal == true and phraseHighlight == true
  float boost;  // valid if terminal == true
  Map&lt;String, QueryPhraseMap&gt; subMap;
} 
</pre>
<p><code>QueryPhraseMap</code> has subMap. The key of the subMap is a term 
text in the user query and the value is a subsequent <code>QueryPhraseMap</code>.
If the query is a term (not phrase), then the subsequent <code>QueryPhraseMap</code>
is marked as terminal. If the query is a phrase, then the subsequent <code>QueryPhraseMap</code>
is not a terminal and it has the next term text in the phrase.</p>

<p>From the sample user query, the following <code>QueryPhraseMap</code> 
will be generated:</p>
<pre>
   QueryPhraseMap
+--------+-+  +-------+-+
|"Lucene"|o+->|boost=2|*|  * : terminal
+--------+-+  +-------+-+

+--------+-+  +---------+-+  +-------+------+-+
|"search"|o+->|"library"|o+->|boost=1|slop=1|*|
+--------+-+  +---------+-+  +-------+------+-+
</pre>

<h3>Step 2.</h3>
<p>In Step 2, Fast Vector Highlighter generates {@link org.apache.lucene.search.vectorhighlight.FieldTermStack}. Fast Vector Highlighter uses term vector data
(must be stored {@link org.apache.lucene.document.FieldType#setStoreTermVectorOffsets(boolean)} and {@link org.apache.lucene.document.FieldType#setStoreTermVectorPositions(boolean)})
to generate it. <code>FieldTermStack</code> keeps the terms in the user query.
Therefore, in this sample case, Fast Vector Highlighter generates the following <code>FieldTermStack</code>:</p>
<pre>
   FieldTermStack
+------------------+
|"Lucene"(0,6,0)   |
+------------------+
|"search"(12,18,3) |
+------------------+
|"library"(26,33,5)|
+------------------+
where : "termText"(startOffset,endOffset,position)
</pre>
<h3>Step 3.</h3>
<p>In Step 3, Fast Vector Highlighter generates {@link org.apache.lucene.search.vectorhighlight.FieldPhraseList}
by reference to <code>QueryPhraseMap</code> and <code>FieldTermStack</code>.</p>
<pre>
   FieldPhraseList
+----------------+-----------------+---+
|"Lucene"        |[(0,6)]          |w=2|
+----------------+-----------------+---+
|"search library"|[(12,18),(26,33)]|w=1|
+----------------+-----------------+---+
</pre>
<p>The type of each entry is <code>WeightedPhraseInfo</code> that consists of
an array of terms offsets and weight. 
</p>
<h3>Step 4.</h3>
<p>In Step 4, Fast Vector Highlighter creates <code>FieldFragList</code> by reference to
<code>FieldPhraseList</code>. In this sample case, the following
<code>FieldFragList</code> will be generated:</p>
<pre>
   FieldFragList
+---------------------------------+
|"Lucene"[(0,6)]                  |
|"search library"[(12,18),(26,33)]|
|totalBoost=3                     |
+---------------------------------+
</pre>

<p>
The calculation for each <code>FieldFragList.WeightedFragInfo.totalBoost</code> (weight)  
depends on the implementation of <code>FieldFragList.add( ... )</code>:
<pre class="prettyprint">
  public void add( int startOffset, int endOffset, List&lt;WeightedPhraseInfo&gt; phraseInfoList ) {
    float totalBoost = 0;
    List&lt;SubInfo&gt; subInfos = new ArrayList&lt;SubInfo&gt;();
    for( WeightedPhraseInfo phraseInfo : phraseInfoList ){
      subInfos.add( new SubInfo( phraseInfo.getText(), phraseInfo.getTermsOffsets(), phraseInfo.getSeqnum() ) );
      totalBoost += phraseInfo.getBoost();
    }
    getFragInfos().add( new WeightedFragInfo( startOffset, endOffset, subInfos, totalBoost ) );
  }
  
</pre>
The used implementation of <code>FieldFragList</code> is noted in <code>BaseFragListBuilder.createFieldFragList( ... )</code>:
<pre class="prettyprint">
  public FieldFragList createFieldFragList( FieldPhraseList fieldPhraseList, int fragCharSize ){
    return createFieldFragList( fieldPhraseList, new SimpleFieldFragList( fragCharSize ), fragCharSize );
  }
</pre>
<p>
Currently there are basically to approaches available:
</p>
<ul>
<li><code>SimpleFragListBuilder using SimpleFieldFragList</code>: <i>sum-of-boosts</i>-approach. The totalBoost is calculated by summarizing the query-boosts per term. Per default a term is boosted by 1.0</li>
<li><code>WeightedFragListBuilder using WeightedFieldFragList</code>: <i>sum-of-distinct-weights</i>-approach. The totalBoost is calculated by summarizing the IDF-weights of distinct terms.</li>
</ul> 
<p>Comparison of the two approaches:</p>
<table border="1">
<caption>
	query = das alte testament (The Old Testament)
</caption>
<tr><th>Terms in fragment</th><th>sum-of-distinct-weights</th><th>sum-of-boosts</th></tr>
<tr><td>das alte testament</td><td>5.339621</td><td>3.0</td></tr>
<tr><td>das alte testament</td><td>5.339621</td><td>3.0</td></tr>
<tr><td>das testament alte</td><td>5.339621</td><td>3.0</td></tr>
<tr><td>das alte testament</td><td>5.339621</td><td>3.0</td></tr>
<tr><td>das testament</td><td>2.9455688</td><td>2.0</td></tr>
<tr><td>das alte</td><td>2.4759595</td><td>2.0</td></tr>
<tr><td>das das das das</td><td>1.5015357</td><td>4.0</td></tr>
<tr><td>das das das</td><td>1.3003681</td><td>3.0</td></tr>
<tr><td>das das</td><td>1.061746</td><td>2.0</td></tr>
<tr><td>alte</td><td>1.0</td><td>1.0</td></tr>
<tr><td>alte</td><td>1.0</td><td>1.0</td></tr>
<tr><td>das</td><td>0.7507678</td><td>1.0</td></tr>
<tr><td>das</td><td>0.7507678</td><td>1.0</td></tr>
<tr><td>das</td><td>0.7507678</td><td>1.0</td></tr>
<tr><td>das</td><td>0.7507678</td><td>1.0</td></tr>
<tr><td>das</td><td>0.7507678</td><td>1.0</td></tr>
</table>

<h3>Step 5.</h3>
<p>In Step 5, by using <code>FieldFragList</code> and the field stored data,
Fast Vector Highlighter creates highlighted snippets!</p>
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